GIScience & Remote Sensing (Dec 2022)
Multi-spatiotemporal heterogeneous legacy effects of climate on terrestrial vegetation dynamics in China
Abstract
Investigating vegetation–climate interactions is critical for understanding behavioral patterns of terrestrial ecosystems and formulating food security strategies. However, the multi-spatiotemporal legacy effects of climate on terrestrial vegetation remain unclear. In this study, we examined the dynamic trends of vegetation distribution and climatic factors at multiple temporal and spatial scales. Moreover, using cross-wavelet transform, wavelet coherence transform, and partial correlation analysis, a paradigm framework was established to determine the multi-spatiotemporal legacy effect of climate on vegetation in China in 2000–2019, as well as the response of different vegetation types. The results indicate a significant greening trend in China, accompanied by a warming and wetting pattern over the past 20 years. The phase difference of the wavelet coherence transform in the time-frequency domain revealed remarkable legacy effects and regional variations, indicating a complicated relationship between vegetation and climate. Meanwhile, different vegetation types exhibited heterogeneous responses of legacy effect of precipitation and temperature on multi-spatiotemporal scales; moreover, the lag time in spring was shorter than that in summer and autumn. The average legacy effect on different vegetation types was approximately 1–2 months. Therefore, the heterogeneity of the legacy effects is a complicated process of dynamic variation, which can be summarized as the comprehensive characteristics of vegetation response to climate with regional discrepancy, ecosystem category, and multi-temporal scale. These findings advance our understanding regarding the preference of vegetation to hydrothermal conditions across biomes and ecosystems and provide a future framework for elucidating the dynamic response of vegetation to other more complex factors in this warmer world. Furthermore, our results emphasize that multi-spatiotemporal legacy effects should be incorporated into the vegetation–climate interaction model and the formulation of macro-environmental management policies.
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